Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	118,267
2	South Dakota	107,469
3	Iowa	85,175
4	Wisconsin	84,904
5	Nebraska	81,377
6	Utah	78,847
7	Rhode Island	75,735
8	Tennessee	75,432
9	Idaho	73,566
10	Wyoming	72,772
11	Montana	72,471
12	Illinois	71,569
13	Kansas	71,373
14	Minnesota	70,814
15	Indiana	69,980
16	Arkansas	67,302
17	Nevada	66,943
18	Oklahoma	66,574
19	Alabama	66,249
20	Mississippi	65,688
21	Missouri	63,627
22	Arizona	63,382
23	New Mexico	62,383
24	Louisiana	61,792
25	Alaska	59,874
26	Florida	56,457
27	Texas	55,529
28	Kentucky	55,340
29	Colorado	54,302
30	Ohio	53,841
31	Georgia	53,807
32	South Carolina	53,553
33	Delaware	52,431
34	Michigan	50,111
35	New Jersey	49,275
36	California	48,923
37	Massachusetts	47,497
38	Connecticut	46,946
39	North Carolina	46,196
40	Pennsylvania	44,478
41	New York	44,251
42	Maryland	41,905
43	West Virginia	40,921
44	District of Columbia	37,888
45	Virginia	36,423
46	Puerto Rico	32,083
47	Washington	30,288
48	New Hampshire	27,497
49	Oregon	24,629
50	Hawaii	14,424
51	Maine	14,346
52	Vermont	10,471

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Tennessee	1,212
2	California	1,041
3	Oklahoma	983
4	Arizona	792
5	Rhode Island	761
6	Indiana	747
7	Ohio	669
8	Delaware	666
9	Nevada	666
10	West Virginia	666
11	Pennsylvania	650
12	Utah	646
13	Arkansas	628
14	Alabama	621
15	Massachusetts	602
16	New Hampshire	595
17	Mississippi	569
18	South Carolina	560
19	Idaho	553
20	New Mexico	525
21	North Carolina	518
22	Kentucky	514
23	New York	493
24	Kansas	490
25	South Dakota	490
26	Illinois	485
27	Florida	482
28	New Jersey	470
29	Georgia	461
30	Wisconsin	457
31	Missouri	452
32	Virginia	449
33	Texas	439
34	Minnesota	438
35	Wyoming	437
36	Montana	436
37	Connecticut	429
38	Iowa	423
39	Colorado	410
40	Nebraska	404
41	Maryland	359
42	Louisiana	346
43	Alaska	304
44	Michigan	304
45	District of Columbia	300
46	Puerto Rico	283
47	Oregon	282
48	Washington	264
49	North Dakota	246
50	Maine	235
51	Vermont	155
52	Hawaii	114

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,051
2	New York	1,858
3	Massachusetts	1,706
4	North Dakota	1,625
5	Connecticut	1,592
6	Rhode Island	1,576
7	South Dakota	1,561
8	Louisiana	1,528
9	Mississippi	1,482
10	Illinois	1,304
11	Michigan	1,216
12	Iowa	1,137
13	Indiana	1,104
14	Arizona	1,095
15	Pennsylvania	1,095
16	Arkansas	1,091
17	District of Columbia	1,051
18	New Mexico	1,039
19	South Carolina	963
20	Florida	962
21	Georgia	948
22	Nevada	909
23	Maryland	904
24	Texas	902
25	Alabama	895
26	Delaware	894
27	Tennessee	889
28	Minnesota	874
29	Missouri	862
30	Kansas	840
31	Montana	824
32	Wisconsin	816
33	Nebraska	790
34	Colorado	769
35	Idaho	730
36	Ohio	694
37	West Virginia	629
38	Wyoming	606
39	Kentucky	604
40	North Carolina	599
41	California	581
42	Oklahoma	560
43	Virginia	545
44	New Hampshire	482
45	Puerto Rico	432
46	Washington	418
47	Utah	362
48	Oregon	320
49	Alaska	240
50	Maine	217
51	Hawaii	197
52	Vermont	177

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	South Dakota	19
2	Arkansas	17
3	Iowa	14
4	Rhode Island	14
5	Kansas	12
6	Michigan	9
7	Pennsylvania	9
8	Connecticut	8
9	Illinois	8
10	Indiana	8
11	Louisiana	8
12	Minnesota	8
13	Montana	8
14	New Mexico	8
15	Tennessee	8
16	Arizona	7
17	Massachusetts	7
18	Nevada	7
19	West Virginia	7
20	Alabama	6
21	Colorado	6
22	District of Columbia	6
23	Kentucky	6
24	Maryland	6
25	Mississippi	6
26	Missouri	6
27	Nebraska	6
28	New York	6
29	Texas	6
30	Wisconsin	6
31	Delaware	5
32	Puerto Rico	5
33	South Carolina	5
34	California	4
35	Florida	4
36	Idaho	4
37	New Hampshire	4
38	Ohio	4
39	Oklahoma	4
40	New Jersey	3
41	North Carolina	3
42	North Dakota	3
43	Oregon	3
44	Maine	2
45	Utah	2
46	Vermont	2
47	Virginia	2
48	Georgia	1
49	Alaska	0
50	Hawaii	0
51	Washington	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	260,353	1	99
Norton	Kansas	214,699	2	99
Lincoln	Arkansas	210,458	3	99
Bon Homme	South Dakota	209,535	4	99
Dewey	South Dakota	209,097	5	99
Davidson	Tennessee	90,049	374	88
Richland	South Carolina	58,697	1521	51
York	South Carolina	47,861	2090	33
Orange	California	41,103	2376	24
Pierce	Washington	27,683	2808	10

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	6,213	4	99
Foster	North Dakota	5,919	5	99
Richland	South Carolina	774	1711	45
Davidson	Tennessee	746	1768	43
York	South Carolina	605	2039	35
Orange	California	560	2125	32
Pierce	Washington	337	2568	18

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.